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＜E-Book＞
Probability Theory, Random Processes and Mathematical Statistics / by Y. Rozanov
(Mathematics and Its Applications ; 344)

Edition	1st ed. 1995.
Publisher	(Dordrecht : Springer Netherlands : Imprint: Springer)
Year	1995
Language	English
Size	XI, 259 p : online resource
Authors	*Rozanov, Y author SpringerLink (Online service)
Subjects	LCSH:Statistics  LCSH:Functional analysis LCSH:Mathematical models LCSH:Differential equations FREE:Statistics FREE:Functional Analysis FREE:Mathematical Modeling and Industrial Mathematics FREE:Differential Equations
Notes	1. Introductory Probability Theory -- 1. The Notion of Probability -- 2. Some Probability Models -- 3. Random Variables -- 4. Mathematical Expectation -- 5. Correlation -- 6. Characteristic Functions -- 7. The Central Limit Theorem -- 2. Random Processes -- 1. Random Processes with Discrete State Space -- 2. Random Processes with Continuous States -- 3. An Introduction to Mathematical Statistics -- 1. Some Examples of Statistical Problems and Methods -- 2. Optimality of Statistical Decisions -- 4. Basic Elements of Probability Theory -- 1. General Probability Distributions -- 2. Conditional Probabilities and Expectations -- 3. Conditional Expectations and Martingales -- 5. Elements of Stochastic Analysis and Stochastic Differential Equations -- 1. Stochastic Series -- 2. Stochastic Integrals -- 3. Stochastic Integral Representations -- 4. Stochastic Differential Equations Probability Theory, Theory of Random Processes and Mathematical Statistics are important areas of modern mathematics and its applications. They develop rigorous models for a proper treatment for various 'random' phenomena which we encounter in the real world. They provide us with numerous tools for an analysis, prediction and, ultimately, control of random phenomena. Statistics itself helps with choice of a proper mathematical model (e.g., by estimation of unknown parameters) on the basis of statistical data collected by observations. This volume is intended to be a concise textbook for a graduate level course, with carefully selected topics representing the most important areas of modern Probability, Random Processes and Statistics. The first part (Ch. 1-3) can serve as a self-contained, elementary introduction to Probability, Random Processes and Statistics. It contains a number of relatively sim­ ple and typical examples of random phenomena which allow a natural introduction of general structures and methods. Only knowledge of elements of real/complex analysis, linear algebra and ordinary differential equations is required here. The second part (Ch. 4-6) provides a foundation of Stochastic Analysis, gives information on basic models of random processes and tools to study them. Here a familiarity with elements of functional analysis is necessary. Our intention to make this course fast-moving made it necessary to present important material in a form of examples HTTP:URL=https://doi.org/10.1007/978-94-011-0449-4

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